The Forrester Wave for Master Data Management went live today. The results may surprise you.
MDM tools today don’t look like your father’s MDM. No longer an integration hub between applications and DBMSs, today’s tools are transitioning or have reinvented MDM to handle the context missing from system traditional implementations. Visualizations, graph repositories, big data and cloud scale, along with application like interfaces for nontechnical users, mean MDM and master data gets personal with stakeholders.
Semantics and insight are not an outcome of MDM but an integrated part of the engine and hub. Three MDM evolutions stand out:
- Business-defined views of data: For graph-based vendors such as Reltio and Pitney Bowes, master domains are shaped by business use cases. For example, customer master can be defined beyond the bounds of a household, identity, and account. Customer behavioral characteristics can be the starting points for taxonomies and hierarchies. Integration of master domains is based on physical, logical, linkage, and semantic schemas for a more seamless navigation and querying of master data to align with the explosion of data views created by analytics, applications, and microservices.
- Visualization and navigation for stewardship: Knowledge graphs provide contextual windows into master data domains and the links between domains, as seen in tools from IBM, Informatica, and Orchestra Networks. For example, securities can be viewed within the context of mutual fund and other investment products and services. In some cases, admins and stewards can click through master data objects to view details and make changes or create new links the way you would connect objects in a Visio or Powerpoint application.
- Valuation of data and conditions: MDM tools are better able to handle business measures and metrics that provide an understanding of business impact from master data. For example, revenue, credit risk and customer loyalty values can be used to set governance thresholds that trigger alerts and stewardship tasks. Governance teams also have the ability to measure the compliance of master data to data policies in context of those business outcomes and objectives — giving stakeholders a better perspective on data governance effectiveness and contribution.
These evolutions create a new landscape and vision for MDM that is based on business value and context over data management efficiency and integration effectiveness. However, that doesn’t mean the workhorse that drives scalable orchestration and syndication is ignored. Vendors in the MDM Wave needed to meet enterprise scale requirements demanded by more diverse ecosystems within traditional environments as well as big data and the cloud. So, as you embark on the MDM journey and begin to evaluate vendors and their tools, don’t overlook the innovations being introduced in favor of a workhorse. To do so means that your MDM effort will be out of date and misaligned with business scenarios quickly after launch.